{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3ddbb763",
   "metadata": {},
   "source": [
    "Please run those two cells before running the Notebook!\n",
    "\n",
    "As those plotting settings are standard throughout the book, we do not show them in the book every time we plot something."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad411993",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-29T10:48:06.120195Z",
     "start_time": "2020-01-29T10:48:05.814125Z"
    }
   },
   "outputs": [],
   "source": [
    "# %matplotlib inline\n",
    "%config InlineBackend.figure_format = \"retina\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "280fdc8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-29T10:48:13.141309Z",
     "start_time": "2020-01-29T10:48:13.137453Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
    "\n",
    "# feel free to modify, for example, change the context to \"notebook\"\n",
    "sns.set_theme(context=\"talk\", style=\"whitegrid\", \n",
    "              palette=\"colorblind\", color_codes=True, \n",
    "              rc={\"figure.figsize\": [12, 8]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 2 - Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Converting prices to returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to do it..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Import the libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-30T14:56:23.665431Z",
     "start_time": "2019-11-30T14:56:23.662859Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import yfinance as yf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Download the data and keep the adjusted close prices only:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-30T14:56:26.081194Z",
     "start_time": "2019-11-30T14:56:25.792578Z"
    }
   },
   "outputs": [],
   "source": [
    "df = yf.download(\"AAPL\", \n",
    "                 start=\"2010-01-01\", \n",
    "                 end=\"2020-12-31\",\n",
    "                 progress=False)\n",
    "\n",
    "df = df.loc[:, [\"Adj Close\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Convert adjusted close prices to simple and log returns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-30T14:56:27.550400Z",
     "start_time": "2019-11-30T14:56:27.543542Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"simple_rtn\"] = df[\"Adj Close\"].pct_change()\n",
    "df[\"log_rtn\"] = np.log(df[\"Adj Close\"]/df[\"Adj Close\"].shift(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Inspect the output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-30T14:56:28.623109Z",
     "start_time": "2019-11-30T14:56:28.615548Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>simple_rtn</th>\n",
       "      <th>log_rtn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>6.444379</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>6.544686</td>\n",
       "      <td>0.015565</td>\n",
       "      <td>0.015445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>6.556003</td>\n",
       "      <td>0.001729</td>\n",
       "      <td>0.001728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>6.451722</td>\n",
       "      <td>-0.015906</td>\n",
       "      <td>-0.016034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>6.439794</td>\n",
       "      <td>-0.001849</td>\n",
       "      <td>-0.001851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close  simple_rtn   log_rtn\n",
       "Date                                       \n",
       "2009-12-31   6.444379         NaN       NaN\n",
       "2010-01-04   6.544686    0.015565  0.015445\n",
       "2010-01-05   6.556003    0.001729  0.001728\n",
       "2010-01-06   6.451722   -0.015906 -0.016034\n",
       "2010-01-07   6.439794   -0.001849 -0.001851"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Adjusting the returns for inflation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to do it..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0. Recreating the steps from the previous recipe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "\n",
    "df = yf.download(\"AAPL\", \n",
    "                 start=\"2010-01-01\", \n",
    "                 end=\"2020-12-31\",\n",
    "                 progress=False)\n",
    "\n",
    "df = df.loc[:, [\"Adj Close\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Import libraries and authenticate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-30T14:52:47.939066Z",
     "start_time": "2019-11-30T14:52:47.902169Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import nasdaqdatalink\n",
    "\n",
    "nasdaqdatalink.ApiConfig.api_key = \"YOUR_KEY_HERE\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-17T19:59:22.672136Z",
     "start_time": "2019-04-17T19:59:21.804305Z"
    }
   },
   "source": [
    "2. Resample daily prices to monthly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>6.434927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>5.864813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-28</th>\n",
       "      <td>6.248349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-31</th>\n",
       "      <td>7.176042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-30</th>\n",
       "      <td>7.972735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31</th>\n",
       "      <td>127.677979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>114.587616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31</th>\n",
       "      <td>107.710991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>117.996666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>132.536850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Adj Close\n",
       "Date                  \n",
       "2009-12-31    6.434927\n",
       "2010-01-31    5.864813\n",
       "2010-02-28    6.248349\n",
       "2010-03-31    7.176042\n",
       "2010-04-30    7.972735\n",
       "...                ...\n",
       "2020-08-31  127.677979\n",
       "2020-09-30  114.587616\n",
       "2020-10-31  107.710991\n",
       "2020-11-30  117.996666\n",
       "2020-12-31  132.536850\n",
       "\n",
       "[133 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.resample(\"M\").last()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Download inflation data from Nasdaq Data Link:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-30T15:04:08.530881Z",
     "start_time": "2019-11-30T15:04:07.551059Z"
    }
   },
   "outputs": [],
   "source": [
    "df_cpi = (\n",
    "    nasdaqdatalink.get(dataset=\"RATEINF/CPI_USA\", \n",
    "                       start_date=\"2009-12-01\", \n",
    "                       end_date=\"2020-12-31\")\n",
    "    .rename(columns={\"Value\": \"cpi\"})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>215.949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>216.687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-28</th>\n",
       "      <td>216.741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-31</th>\n",
       "      <td>217.631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-30</th>\n",
       "      <td>218.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31</th>\n",
       "      <td>259.918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>260.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31</th>\n",
       "      <td>260.388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>260.229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>260.474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                cpi\n",
       "Date               \n",
       "2009-12-31  215.949\n",
       "2010-01-31  216.687\n",
       "2010-02-28  216.741\n",
       "2010-03-31  217.631\n",
       "2010-04-30  218.009\n",
       "...             ...\n",
       "2020-08-31  259.918\n",
       "2020-09-30  260.280\n",
       "2020-10-31  260.388\n",
       "2020-11-30  260.229\n",
       "2020-12-31  260.474\n",
       "\n",
       "[133 rows x 1 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cpi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "4. Join inflation data to prices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-30T15:04:09.581935Z",
     "start_time": "2019-11-30T15:04:09.578186Z"
    }
   },
   "outputs": [],
   "source": [
    "df = df.join(df_cpi, how=\"left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Calculate simple returns and inflation rate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-30T15:04:10.604378Z",
     "start_time": "2019-11-30T15:04:10.597383Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"simple_rtn\"] = df[\"Adj Close\"].pct_change()\n",
    "df[\"inflation_rate\"] = df[\"cpi\"].pct_change()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Adjust the returns for inflation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-30T15:04:11.654169Z",
     "start_time": "2019-11-30T15:04:11.642959Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>cpi</th>\n",
       "      <th>simple_rtn</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>real_rtn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>6.434927</td>\n",
       "      <td>215.949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>5.864813</td>\n",
       "      <td>216.687</td>\n",
       "      <td>-0.088597</td>\n",
       "      <td>0.003417</td>\n",
       "      <td>-0.091701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-28</th>\n",
       "      <td>6.248349</td>\n",
       "      <td>216.741</td>\n",
       "      <td>0.065396</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>0.065131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-31</th>\n",
       "      <td>7.176042</td>\n",
       "      <td>217.631</td>\n",
       "      <td>0.148470</td>\n",
       "      <td>0.004106</td>\n",
       "      <td>0.143773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-30</th>\n",
       "      <td>7.972735</td>\n",
       "      <td>218.009</td>\n",
       "      <td>0.111021</td>\n",
       "      <td>0.001737</td>\n",
       "      <td>0.109095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close      cpi  simple_rtn  inflation_rate  real_rtn\n",
       "Date                                                                \n",
       "2009-12-31   6.434927  215.949         NaN             NaN       NaN\n",
       "2010-01-31   5.864813  216.687   -0.088597        0.003417 -0.091701\n",
       "2010-02-28   6.248349  216.741    0.065396        0.000249  0.065131\n",
       "2010-03-31   7.176042  217.631    0.148470        0.004106  0.143773\n",
       "2010-04-30   7.972735  218.009    0.111021        0.001737  0.109095"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"real_rtn\"] = (\n",
    "    (df[\"simple_rtn\"] + 1) / (df[\"inflation_rate\"] + 1) - 1\n",
    ")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There's more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Import the library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pff2/lib/python3.9/site-packages/cpi/__init__.py:46: StaleDataWarning: CPI data is out of date. To accurately inflate to today's dollars, you must run `cpi.update()`.\n",
      "  warnings.warn(StaleDataWarning())\n"
     ]
    }
   ],
   "source": [
    "import cpi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in the case of seeing the `StaleDataWarning: CPI data is out of date`\n",
    "cpi.update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Obtain the default CPI series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Series: CUUR0000SA0: All items in U.S. city average, all urban consumers, not seasonally adjusted>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpi_series = cpi.series.get()\n",
    "cpi_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUUR0000SA0: All items in U.S. city average, all urban consumers, not seasonally adjusted\n"
     ]
    }
   ],
   "source": [
    "print(cpi_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Convert the object into a `pandas` DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cpi_2 = cpi_series.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Filter the DataFrame and view the top 12 observations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>216.687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>216.741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-01</th>\n",
       "      <td>217.631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>218.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-01</th>\n",
       "      <td>218.178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-01</th>\n",
       "      <td>217.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>218.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-01</th>\n",
       "      <td>218.312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-01</th>\n",
       "      <td>218.439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>218.711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>218.803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-01</th>\n",
       "      <td>219.179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              value\n",
       "date               \n",
       "2010-01-01  216.687\n",
       "2010-02-01  216.741\n",
       "2010-03-01  217.631\n",
       "2010-04-01  218.009\n",
       "2010-05-01  218.178\n",
       "2010-06-01  217.965\n",
       "2010-07-01  218.011\n",
       "2010-08-01  218.312\n",
       "2010-09-01  218.439\n",
       "2010-10-01  218.711\n",
       "2010-11-01  218.803\n",
       "2010-12-01  219.179"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cpi_2.query(\"period_type == 'monthly' and year >= 2010\") \\\n",
    "        .loc[:, [\"date\", \"value\"]] \\\n",
    "        .set_index(\"date\") \\\n",
    "        .head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Changing the frequency of time series data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to do it..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0. Obtain the log returns in case of starting in this recipe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-18T19:31:22.287848Z",
     "start_time": "2020-01-18T19:31:21.259418Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>log_rtn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>0.085034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-0.088077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>0.014527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>-0.090514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>0.046281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             log_rtn\n",
       "Date                \n",
       "2000-01-03  0.085034\n",
       "2000-01-04 -0.088077\n",
       "2000-01-05  0.014527\n",
       "2000-01-06 -0.090514\n",
       "2000-01-07  0.046281"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import yfinance as yf\n",
    "import numpy as np\n",
    "\n",
    "# download data \n",
    "df = yf.download(\"AAPL\", \n",
    "                 start=\"2000-01-01\", \n",
    "                 end=\"2010-12-31\", \n",
    "                 auto_adjust=False,\n",
    "                 progress=False)\n",
    "\n",
    "# keep only the adjusted close price\n",
    "df = df.loc[:, [\"Adj Close\"]] \\\n",
    "       .rename(columns={\"Adj Close\": \"adj_close\"})\n",
    "\n",
    "# calculate simple returns\n",
    "df[\"log_rtn\"] = np.log(df[\"adj_close\"]/df[\"adj_close\"].shift(1))\n",
    "\n",
    "# remove redundant data\n",
    "df = df.drop(\"adj_close\", axis=1) \\\n",
    "       .dropna(axis=0)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-17T19:59:24.710535Z",
     "start_time": "2019-04-17T19:59:23.359839Z"
    }
   },
   "source": [
    "1. Import the libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-18T19:31:22.518995Z",
     "start_time": "2020-01-18T19:31:22.516127Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-17T19:59:24.710535Z",
     "start_time": "2019-04-17T19:59:23.359839Z"
    }
   },
   "source": [
    "2. Define the function for calculating the realized volatility:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-18T19:31:23.284265Z",
     "start_time": "2020-01-18T19:31:23.281316Z"
    }
   },
   "outputs": [],
   "source": [
    "def realized_volatility(x):\n",
    "    return np.sqrt(np.sum(x**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Calculate monthly realized volatility:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-18T19:31:23.828166Z",
     "start_time": "2020-01-18T19:31:23.658707Z"
    }
   },
   "outputs": [],
   "source": [
    "df_rv = (\n",
    "    df.groupby(pd.Grouper(freq=\"M\"))\n",
    "    .apply(realized_volatility)\n",
    "    .rename(columns={\"log_rtn\": \"rv\"})\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Annualize the values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-18T19:31:24.018004Z",
     "start_time": "2020-01-18T19:31:24.013998Z"
    }
   },
   "outputs": [],
   "source": [
    "df_rv.rv = df_rv[\"rv\"] * np.sqrt(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Plot the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-18T19:33:05.785614Z",
     "start_time": "2020-01-18T19:33:03.920890Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 551,
       "width": 839
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, sharex=True)\n",
    "ax[0].plot(df)\n",
    "ax[0].set_title(\"Apple's log returns (2000-2012)\")\n",
    "ax[1].plot(df_rv)\n",
    "ax[1].set_title(\"Annualized realized volatility\")\n",
    "\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "# plt.savefig('images/figure_2_5', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 Different ways of imputing missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to do it..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Import the libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nasdaqdatalink"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Download the inflation data from Nasdaq Data Link:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "nasdaqdatalink.ApiConfig.api_key = \"YOUR_KEY_HERE\" \n",
    "\n",
    "df = (\n",
    "    nasdaqdatalink.get(dataset=\"RATEINF/CPI_USA\", \n",
    "                       start_date=\"2015-01-01\", \n",
    "                       end_date=\"2020-12-31\")\n",
    "    .rename(columns={\"Value\": \"cpi\"})\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Introduce 5 missing values at random:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "      <th>cpi_missing</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>233.707</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>234.722</td>\n",
       "      <td>234.722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>236.119</td>\n",
       "      <td>236.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>236.599</td>\n",
       "      <td>236.599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>237.805</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                cpi  cpi_missing\n",
       "Date                            \n",
       "2015-01-31  233.707          NaN\n",
       "2015-02-28  234.722      234.722\n",
       "2015-03-31  236.119      236.119\n",
       "2015-04-30  236.599      236.599\n",
       "2015-05-31  237.805          NaN"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "rand_indices = np.random.choice(df.index, 5, replace=False)\n",
    "\n",
    "df[\"cpi_missing\"] = df.loc[:, \"cpi\"]\n",
    "df.loc[rand_indices, \"cpi_missing\"] = np.nan\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Fill the missing values using different methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for method in [\"bfill\", \"ffill\"]:\n",
    "    df[f\"method_{method}\"] = (\n",
    "        df[[\"cpi_missing\"]].fillna(method=method)\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Inspect the results by displaying the rows in which we created the missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "      <th>cpi_missing</th>\n",
       "      <th>method_bfill</th>\n",
       "      <th>method_ffill</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>233.707</td>\n",
       "      <td>NaN</td>\n",
       "      <td>234.722</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>237.805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>238.638</td>\n",
       "      <td>236.599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-31</th>\n",
       "      <td>240.647</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240.849</td>\n",
       "      <td>241.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-31</th>\n",
       "      <td>244.733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>244.955</td>\n",
       "      <td>244.524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>258.115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>256.389</td>\n",
       "      <td>258.678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                cpi  cpi_missing  method_bfill  method_ffill\n",
       "Date                                                        \n",
       "2015-01-31  233.707          NaN       234.722           NaN\n",
       "2015-05-31  237.805          NaN       238.638       236.599\n",
       "2016-07-31  240.647          NaN       240.849       241.038\n",
       "2017-05-31  244.733          NaN       244.955       244.524\n",
       "2020-03-31  258.115          NaN       256.389       258.678"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[rand_indices].sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Plot the results for years 2015-2016:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 550,
       "width": 839
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[:\"2017-01-01\"] \\\n",
    "  .drop(columns=[\"cpi_missing\"]) \\\n",
    "  .plot(title=\"Different ways of filling missing values\");\n",
    "\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "# plt.savefig('images/figure_2_8', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There's more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Use linear interpolation to fill the missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"method_interpolate\"] = df[[\"cpi_missing\"]].interpolate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Inspect the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "      <th>cpi_missing</th>\n",
       "      <th>method_bfill</th>\n",
       "      <th>method_ffill</th>\n",
       "      <th>method_interpolate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>233.707</td>\n",
       "      <td>NaN</td>\n",
       "      <td>234.722</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>237.805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>238.638</td>\n",
       "      <td>236.599</td>\n",
       "      <td>237.6185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-31</th>\n",
       "      <td>240.647</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240.849</td>\n",
       "      <td>241.038</td>\n",
       "      <td>240.9435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-31</th>\n",
       "      <td>244.733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>244.955</td>\n",
       "      <td>244.524</td>\n",
       "      <td>244.7395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>258.115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>256.389</td>\n",
       "      <td>258.678</td>\n",
       "      <td>257.5335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                cpi  cpi_missing  method_bfill  method_ffill  \\\n",
       "Date                                                           \n",
       "2015-01-31  233.707          NaN       234.722           NaN   \n",
       "2015-05-31  237.805          NaN       238.638       236.599   \n",
       "2016-07-31  240.647          NaN       240.849       241.038   \n",
       "2017-05-31  244.733          NaN       244.955       244.524   \n",
       "2020-03-31  258.115          NaN       256.389       258.678   \n",
       "\n",
       "            method_interpolate  \n",
       "Date                            \n",
       "2015-01-31                 NaN  \n",
       "2015-05-31            237.6185  \n",
       "2016-07-31            240.9435  \n",
       "2017-05-31            244.7395  \n",
       "2020-03-31            257.5335  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[rand_indices].sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Plot the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 550,
       "width": 839
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[:\"2017-01-01\"] \\\n",
    "  .drop(columns=[\"cpi_missing\"]) \\\n",
    "  .plot(title=\"Different ways of filling missing values\");\n",
    "\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "# plt.savefig('images/figure_2_10', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5 Converting currencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to do it..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Import the libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import yfinance as yf\n",
    "from forex_python.converter import CurrencyRates\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Download Apple's OHLC prices from January 2020:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = yf.download(\"AAPL\", \n",
    "                 start=\"2020-01-01\", \n",
    "                 end=\"2020-01-31\",\n",
    "                 progress=False)\n",
    "df = df.drop(columns=[\"Adj Close\", \"Volume\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Instantiate the `CurrencyRates` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = CurrencyRates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Download the USD/EUR rate for each required date:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"usd_eur\"] = [c.get_rate(\"USD\", \"EUR\", date) for date in df.index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Convert the prices in USD to EUR:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>usd_eur</th>\n",
       "      <th>Open_EUR</th>\n",
       "      <th>High_EUR</th>\n",
       "      <th>Low_EUR</th>\n",
       "      <th>Close_EUR</th>\n",
       "      <th>usd_eur_EUR</th>\n",
       "      <th>Open_EUR_EUR</th>\n",
       "      <th>High_EUR_EUR</th>\n",
       "      <th>Low_EUR_EUR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>72.482</td>\n",
       "      <td>73.420</td>\n",
       "      <td>72.380</td>\n",
       "      <td>73.412</td>\n",
       "      <td>0.890</td>\n",
       "      <td>64.521</td>\n",
       "      <td>65.355</td>\n",
       "      <td>64.429</td>\n",
       "      <td>65.348</td>\n",
       "      <td>0.792</td>\n",
       "      <td>57.433</td>\n",
       "      <td>58.176</td>\n",
       "      <td>57.352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>74.060</td>\n",
       "      <td>75.150</td>\n",
       "      <td>73.798</td>\n",
       "      <td>75.088</td>\n",
       "      <td>0.893</td>\n",
       "      <td>66.166</td>\n",
       "      <td>67.140</td>\n",
       "      <td>65.932</td>\n",
       "      <td>67.084</td>\n",
       "      <td>0.798</td>\n",
       "      <td>59.114</td>\n",
       "      <td>59.984</td>\n",
       "      <td>58.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>74.287</td>\n",
       "      <td>75.145</td>\n",
       "      <td>74.125</td>\n",
       "      <td>74.357</td>\n",
       "      <td>0.897</td>\n",
       "      <td>66.643</td>\n",
       "      <td>67.413</td>\n",
       "      <td>66.498</td>\n",
       "      <td>66.706</td>\n",
       "      <td>0.805</td>\n",
       "      <td>59.786</td>\n",
       "      <td>60.476</td>\n",
       "      <td>59.655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>73.448</td>\n",
       "      <td>74.990</td>\n",
       "      <td>73.188</td>\n",
       "      <td>74.950</td>\n",
       "      <td>0.893</td>\n",
       "      <td>65.613</td>\n",
       "      <td>66.991</td>\n",
       "      <td>65.381</td>\n",
       "      <td>66.956</td>\n",
       "      <td>0.798</td>\n",
       "      <td>58.615</td>\n",
       "      <td>59.846</td>\n",
       "      <td>58.407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>74.960</td>\n",
       "      <td>75.225</td>\n",
       "      <td>74.370</td>\n",
       "      <td>74.598</td>\n",
       "      <td>0.895</td>\n",
       "      <td>67.096</td>\n",
       "      <td>67.334</td>\n",
       "      <td>66.568</td>\n",
       "      <td>66.772</td>\n",
       "      <td>0.801</td>\n",
       "      <td>60.058</td>\n",
       "      <td>60.270</td>\n",
       "      <td>59.585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close  usd_eur  Open_EUR  High_EUR  \\\n",
       "Date                                                                      \n",
       "2019-12-31  72.482  73.420  72.380  73.412    0.890    64.521    65.355   \n",
       "2020-01-02  74.060  75.150  73.798  75.088    0.893    66.166    67.140   \n",
       "2020-01-03  74.287  75.145  74.125  74.357    0.897    66.643    67.413   \n",
       "2020-01-06  73.448  74.990  73.188  74.950    0.893    65.613    66.991   \n",
       "2020-01-07  74.960  75.225  74.370  74.598    0.895    67.096    67.334   \n",
       "\n",
       "            Low_EUR  Close_EUR  usd_eur_EUR  Open_EUR_EUR  High_EUR_EUR  \\\n",
       "Date                                                                      \n",
       "2019-12-31   64.429     65.348        0.792        57.433        58.176   \n",
       "2020-01-02   65.932     67.084        0.798        59.114        59.984   \n",
       "2020-01-03   66.498     66.706        0.805        59.786        60.476   \n",
       "2020-01-06   65.381     66.956        0.798        58.615        59.846   \n",
       "2020-01-07   66.568     66.772        0.801        60.058        60.270   \n",
       "\n",
       "            Low_EUR_EUR  \n",
       "Date                     \n",
       "2019-12-31       57.352  \n",
       "2020-01-02       58.905  \n",
       "2020-01-03       59.655  \n",
       "2020-01-06       58.407  \n",
       "2020-01-07       59.585  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for column in df.columns[:-1]:\n",
    "    df[f\"{column}_EUR\"] = df[column] * df[\"usd_eur\"]\n",
    "df.head().round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There's more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Get the USD exchange rates to 31 available currencies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'EUR': 0.8815232722143864,\n",
       " 'JPY': 115.10930888575459,\n",
       " 'BGN': 1.7240832157968968,\n",
       " 'CZK': 21.573519040902678,\n",
       " 'DKK': 6.557475317348377,\n",
       " 'GBP': 0.7357457686882933,\n",
       " 'HUF': 314.92418899858956,\n",
       " 'PLN': 4.00925599435825,\n",
       " 'RON': 4.360719322990127,\n",
       " 'SEK': 9.313998589562763,\n",
       " 'CHF': 0.9195169252468264,\n",
       " 'ISK': 124.47108603667135,\n",
       " 'NOK': 8.844763751763047,\n",
       " 'HRK': 6.643335684062059,\n",
       " 'RUB': 80.11204160789843,\n",
       " 'TRY': 13.8285437235543,\n",
       " 'AUD': 1.3744710860366711,\n",
       " 'BRL': 5.007757404795486,\n",
       " 'CAD': 1.2688645980253879,\n",
       " 'CNY': 6.317789139633286,\n",
       " 'HKD': 7.8040373765867415,\n",
       " 'IDR': 14342.489421720733,\n",
       " 'INR': 74.58876939351198,\n",
       " 'KRW': 1190.259167842031,\n",
       " 'MXN': 20.193846967559942,\n",
       " 'MYR': 4.185472496473907,\n",
       " 'NZD': 1.470292665726375,\n",
       " 'PHP': 51.11071932299012,\n",
       " 'SGD': 1.3445874471086037,\n",
       " 'THB': 32.264633286318755,\n",
       " 'ZAR': 15.030677009873058}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usd_rates = c.get_rates(\"USD\")\n",
    "usd_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(usd_rates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Download the USD/EUR exchange rate from Yahoo Finance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-12-01</th>\n",
       "      <td>0.83098</td>\n",
       "      <td>0.83724</td>\n",
       "      <td>0.83056</td>\n",
       "      <td>0.83577</td>\n",
       "      <td>0.83577</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-02</th>\n",
       "      <td>0.83605</td>\n",
       "      <td>0.83710</td>\n",
       "      <td>0.82583</td>\n",
       "      <td>0.82720</td>\n",
       "      <td>0.82720</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-03</th>\n",
       "      <td>0.82713</td>\n",
       "      <td>0.82802</td>\n",
       "      <td>0.82440</td>\n",
       "      <td>0.82488</td>\n",
       "      <td>0.82488</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-04</th>\n",
       "      <td>0.82508</td>\n",
       "      <td>0.83029</td>\n",
       "      <td>0.82345</td>\n",
       "      <td>0.82775</td>\n",
       "      <td>0.82775</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-05</th>\n",
       "      <td>0.82795</td>\n",
       "      <td>0.82878</td>\n",
       "      <td>0.82028</td>\n",
       "      <td>0.82055</td>\n",
       "      <td>0.82055</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close  Adj Close  Volume\n",
       "Date                                                             \n",
       "2003-12-01  0.83098  0.83724  0.83056  0.83577    0.83577       0\n",
       "2003-12-02  0.83605  0.83710  0.82583  0.82720    0.82720       0\n",
       "2003-12-03  0.82713  0.82802  0.82440  0.82488    0.82488       0\n",
       "2003-12-04  0.82508  0.83029  0.82345  0.82775    0.82775       0\n",
       "2003-12-05  0.82795  0.82878  0.82028  0.82055    0.82055       0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = yf.download(\"USDEUR=X\", \n",
    "                 start=\"2000-01-01\", \n",
    "                 end=\"2010-12-31\",\n",
    "                 progress=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.6 Different ways of aggregating trade data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to do it..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Import the libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from binance.spot import Spot as Client\n",
    "import pandas as pd \n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Instantiate the Binance client and download the last 500 `BTCEUR` trades:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "spot_client = Client(base_url=\"https://api3.binance.com\")\n",
    "r = spot_client.trades(\"BTCEUR\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Process the downloaded trades into a `pandas` DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>qty</th>\n",
       "      <th>quoteQty</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77999355</td>\n",
       "      <td>33285.50</td>\n",
       "      <td>0.00288</td>\n",
       "      <td>95.862240</td>\n",
       "      <td>2022-02-23 19:41:15.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77999356</td>\n",
       "      <td>33286.84</td>\n",
       "      <td>0.00336</td>\n",
       "      <td>111.843782</td>\n",
       "      <td>2022-02-23 19:41:15.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77999357</td>\n",
       "      <td>33275.29</td>\n",
       "      <td>0.00813</td>\n",
       "      <td>270.528108</td>\n",
       "      <td>2022-02-23 19:41:15.941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77999358</td>\n",
       "      <td>33277.44</td>\n",
       "      <td>0.01001</td>\n",
       "      <td>333.107174</td>\n",
       "      <td>2022-02-23 19:41:17.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77999359</td>\n",
       "      <td>33275.29</td>\n",
       "      <td>0.01001</td>\n",
       "      <td>333.085653</td>\n",
       "      <td>2022-02-23 19:41:17.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>77999850</td>\n",
       "      <td>33268.98</td>\n",
       "      <td>0.00067</td>\n",
       "      <td>22.290217</td>\n",
       "      <td>2022-02-23 19:50:56.806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>77999851</td>\n",
       "      <td>33268.49</td>\n",
       "      <td>0.00249</td>\n",
       "      <td>82.838540</td>\n",
       "      <td>2022-02-23 19:50:56.806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>77999852</td>\n",
       "      <td>33268.98</td>\n",
       "      <td>0.02126</td>\n",
       "      <td>707.298515</td>\n",
       "      <td>2022-02-23 19:50:58.903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>77999853</td>\n",
       "      <td>33268.00</td>\n",
       "      <td>0.00596</td>\n",
       "      <td>198.277280</td>\n",
       "      <td>2022-02-23 19:50:58.903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>77999854</td>\n",
       "      <td>33268.52</td>\n",
       "      <td>0.00150</td>\n",
       "      <td>49.902780</td>\n",
       "      <td>2022-02-23 19:50:59.291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id     price      qty    quoteQty                    time\n",
       "0    77999355  33285.50  0.00288   95.862240 2022-02-23 19:41:15.896\n",
       "1    77999356  33286.84  0.00336  111.843782 2022-02-23 19:41:15.896\n",
       "2    77999357  33275.29  0.00813  270.528108 2022-02-23 19:41:15.941\n",
       "3    77999358  33277.44  0.01001  333.107174 2022-02-23 19:41:17.896\n",
       "4    77999359  33275.29  0.01001  333.085653 2022-02-23 19:41:17.900\n",
       "..        ...       ...      ...         ...                     ...\n",
       "495  77999850  33268.98  0.00067   22.290217 2022-02-23 19:50:56.806\n",
       "496  77999851  33268.49  0.00249   82.838540 2022-02-23 19:50:56.806\n",
       "497  77999852  33268.98  0.02126  707.298515 2022-02-23 19:50:58.903\n",
       "498  77999853  33268.00  0.00596  198.277280 2022-02-23 19:50:58.903\n",
       "499  77999854  33268.52  0.00150   49.902780 2022-02-23 19:50:59.291\n",
       "\n",
       "[500 rows x 5 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = (\n",
    "    pd.DataFrame(r)\n",
    "    .drop(columns=[\"isBuyerMaker\", \"isBestMatch\"])\n",
    ")\n",
    "df[\"time\"] = pd.to_datetime(df[\"time\"], unit=\"ms\")\n",
    "\n",
    "for column in [\"price\", \"qty\", \"quoteQty\"]:\n",
    "    df[column] = pd.to_numeric(df[column])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Define a function aggregating the raw trades information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bars(df, add_time=False):\n",
    "    \"\"\"[summary]\n",
    "\n",
    "    Args:\n",
    "        df ([type]): [description]\n",
    "\n",
    "    Returns:\n",
    "        [type]: [description]\n",
    "    \"\"\"\n",
    "    ohlc = df[\"price\"].ohlc()\n",
    "    vwap = (\n",
    "        df.apply(lambda x: np.average(x[\"price\"], weights=x[\"qty\"]))\n",
    "        .to_frame(\"vwap\")\n",
    "    )\n",
    "    vol = df[\"qty\"].sum().to_frame(\"vol\")\n",
    "    cnt = df[\"qty\"].size().to_frame(\"cnt\")\n",
    "    \n",
    "    if add_time:\n",
    "        time = df[\"time\"].last().to_frame(\"time\")\n",
    "        res = pd.concat([time, ohlc, vwap, vol, cnt], axis=1)\n",
    "    else:\n",
    "        res = pd.concat([ohlc, vwap, vol, cnt], axis=1)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Get time bars:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>vwap</th>\n",
       "      <th>vol</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-02-23 19:41:00</th>\n",
       "      <td>33285.50</td>\n",
       "      <td>33286.84</td>\n",
       "      <td>33254.00</td>\n",
       "      <td>33269.47</td>\n",
       "      <td>33269.239073</td>\n",
       "      <td>0.71050</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-23 19:42:00</th>\n",
       "      <td>33265.24</td>\n",
       "      <td>33265.63</td>\n",
       "      <td>33226.18</td>\n",
       "      <td>33231.76</td>\n",
       "      <td>33237.848521</td>\n",
       "      <td>2.26604</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-23 19:43:00</th>\n",
       "      <td>33234.74</td>\n",
       "      <td>33250.30</td>\n",
       "      <td>33215.73</td>\n",
       "      <td>33240.09</td>\n",
       "      <td>33231.975184</td>\n",
       "      <td>0.70111</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-23 19:44:00</th>\n",
       "      <td>33240.10</td>\n",
       "      <td>33240.10</td>\n",
       "      <td>33216.23</td>\n",
       "      <td>33231.33</td>\n",
       "      <td>33234.523449</td>\n",
       "      <td>0.81760</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-23 19:45:00</th>\n",
       "      <td>33227.68</td>\n",
       "      <td>33286.04</td>\n",
       "      <td>33226.31</td>\n",
       "      <td>33279.94</td>\n",
       "      <td>33245.756842</td>\n",
       "      <td>3.39557</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-23 19:46:00</th>\n",
       "      <td>33270.23</td>\n",
       "      <td>33305.45</td>\n",
       "      <td>33266.54</td>\n",
       "      <td>33279.62</td>\n",
       "      <td>33284.459090</td>\n",
       "      <td>0.46893</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-23 19:47:00</th>\n",
       "      <td>33283.24</td>\n",
       "      <td>33328.46</td>\n",
       "      <td>33273.90</td>\n",
       "      <td>33322.05</td>\n",
       "      <td>33295.109170</td>\n",
       "      <td>0.30348</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-23 19:48:00</th>\n",
       "      <td>33308.50</td>\n",
       "      <td>33333.98</td>\n",
       "      <td>33297.37</td>\n",
       "      <td>33315.46</td>\n",
       "      <td>33315.803656</td>\n",
       "      <td>0.58047</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-23 19:49:00</th>\n",
       "      <td>33310.42</td>\n",
       "      <td>33322.00</td>\n",
       "      <td>33281.73</td>\n",
       "      <td>33294.93</td>\n",
       "      <td>33309.609233</td>\n",
       "      <td>0.22579</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-23 19:50:00</th>\n",
       "      <td>33283.21</td>\n",
       "      <td>33294.20</td>\n",
       "      <td>33268.00</td>\n",
       "      <td>33268.52</td>\n",
       "      <td>33281.789160</td>\n",
       "      <td>0.73358</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         open      high       low     close          vwap  \\\n",
       "time                                                                        \n",
       "2022-02-23 19:41:00  33285.50  33286.84  33254.00  33269.47  33269.239073   \n",
       "2022-02-23 19:42:00  33265.24  33265.63  33226.18  33231.76  33237.848521   \n",
       "2022-02-23 19:43:00  33234.74  33250.30  33215.73  33240.09  33231.975184   \n",
       "2022-02-23 19:44:00  33240.10  33240.10  33216.23  33231.33  33234.523449   \n",
       "2022-02-23 19:45:00  33227.68  33286.04  33226.31  33279.94  33245.756842   \n",
       "2022-02-23 19:46:00  33270.23  33305.45  33266.54  33279.62  33284.459090   \n",
       "2022-02-23 19:47:00  33283.24  33328.46  33273.90  33322.05  33295.109170   \n",
       "2022-02-23 19:48:00  33308.50  33333.98  33297.37  33315.46  33315.803656   \n",
       "2022-02-23 19:49:00  33310.42  33322.00  33281.73  33294.93  33309.609233   \n",
       "2022-02-23 19:50:00  33283.21  33294.20  33268.00  33268.52  33281.789160   \n",
       "\n",
       "                         vol  cnt  \n",
       "time                               \n",
       "2022-02-23 19:41:00  0.71050   52  \n",
       "2022-02-23 19:42:00  2.26604  110  \n",
       "2022-02-23 19:43:00  0.70111   73  \n",
       "2022-02-23 19:44:00  0.81760   21  \n",
       "2022-02-23 19:45:00  3.39557   89  \n",
       "2022-02-23 19:46:00  0.46893   31  \n",
       "2022-02-23 19:47:00  0.30348   32  \n",
       "2022-02-23 19:48:00  0.58047   37  \n",
       "2022-02-23 19:49:00  0.22579   21  \n",
       "2022-02-23 19:50:00  0.73358   34  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grouped_time = df.groupby(pd.Grouper(key=\"time\", freq=\"1Min\"))\n",
    "time_bars = get_bars(df_grouped_time)\n",
    "time_bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Get tick bars:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>vwap</th>\n",
       "      <th>vol</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tick_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-02-23 19:41:57.784</td>\n",
       "      <td>33285.50</td>\n",
       "      <td>33286.84</td>\n",
       "      <td>33254.00</td>\n",
       "      <td>33270.89</td>\n",
       "      <td>33269.220356</td>\n",
       "      <td>0.65723</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-02-23 19:42:29.168</td>\n",
       "      <td>33269.47</td>\n",
       "      <td>33269.47</td>\n",
       "      <td>33243.50</td>\n",
       "      <td>33243.50</td>\n",
       "      <td>33249.679200</td>\n",
       "      <td>0.75967</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-02-23 19:42:43.886</td>\n",
       "      <td>33241.86</td>\n",
       "      <td>33244.71</td>\n",
       "      <td>33226.18</td>\n",
       "      <td>33230.04</td>\n",
       "      <td>33233.334328</td>\n",
       "      <td>1.45604</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-02-23 19:43:37.837</td>\n",
       "      <td>33234.89</td>\n",
       "      <td>33244.70</td>\n",
       "      <td>33215.73</td>\n",
       "      <td>33222.93</td>\n",
       "      <td>33228.599024</td>\n",
       "      <td>0.31810</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-02-23 19:44:28.440</td>\n",
       "      <td>33222.72</td>\n",
       "      <td>33250.30</td>\n",
       "      <td>33216.23</td>\n",
       "      <td>33216.98</td>\n",
       "      <td>33234.604510</td>\n",
       "      <td>1.26310</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022-02-23 19:45:15.153</td>\n",
       "      <td>33226.01</td>\n",
       "      <td>33258.29</td>\n",
       "      <td>33221.43</td>\n",
       "      <td>33233.13</td>\n",
       "      <td>33236.663489</td>\n",
       "      <td>1.87995</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022-02-23 19:46:02.625</td>\n",
       "      <td>33233.13</td>\n",
       "      <td>33286.04</td>\n",
       "      <td>33233.13</td>\n",
       "      <td>33284.14</td>\n",
       "      <td>33256.233756</td>\n",
       "      <td>1.56017</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022-02-23 19:47:47.964</td>\n",
       "      <td>33284.89</td>\n",
       "      <td>33305.45</td>\n",
       "      <td>33266.54</td>\n",
       "      <td>33293.35</td>\n",
       "      <td>33284.115017</td>\n",
       "      <td>0.63715</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2022-02-23 19:49:05.929</td>\n",
       "      <td>33294.13</td>\n",
       "      <td>33333.98</td>\n",
       "      <td>33294.13</td>\n",
       "      <td>33314.63</td>\n",
       "      <td>33314.426886</td>\n",
       "      <td>0.78777</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022-02-23 19:50:59.291</td>\n",
       "      <td>33313.24</td>\n",
       "      <td>33322.00</td>\n",
       "      <td>33268.00</td>\n",
       "      <td>33268.52</td>\n",
       "      <td>33286.482830</td>\n",
       "      <td>0.88389</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              time      open      high       low     close  \\\n",
       "tick_group                                                                   \n",
       "0          2022-02-23 19:41:57.784  33285.50  33286.84  33254.00  33270.89   \n",
       "1          2022-02-23 19:42:29.168  33269.47  33269.47  33243.50  33243.50   \n",
       "2          2022-02-23 19:42:43.886  33241.86  33244.71  33226.18  33230.04   \n",
       "3          2022-02-23 19:43:37.837  33234.89  33244.70  33215.73  33222.93   \n",
       "4          2022-02-23 19:44:28.440  33222.72  33250.30  33216.23  33216.98   \n",
       "5          2022-02-23 19:45:15.153  33226.01  33258.29  33221.43  33233.13   \n",
       "6          2022-02-23 19:46:02.625  33233.13  33286.04  33233.13  33284.14   \n",
       "7          2022-02-23 19:47:47.964  33284.89  33305.45  33266.54  33293.35   \n",
       "8          2022-02-23 19:49:05.929  33294.13  33333.98  33294.13  33314.63   \n",
       "9          2022-02-23 19:50:59.291  33313.24  33322.00  33268.00  33268.52   \n",
       "\n",
       "                    vwap      vol  cnt  \n",
       "tick_group                              \n",
       "0           33269.220356  0.65723   50  \n",
       "1           33249.679200  0.75967   50  \n",
       "2           33233.334328  1.45604   50  \n",
       "3           33228.599024  0.31810   50  \n",
       "4           33234.604510  1.26310   50  \n",
       "5           33236.663489  1.87995   50  \n",
       "6           33256.233756  1.56017   50  \n",
       "7           33284.115017  0.63715   50  \n",
       "8           33314.426886  0.78777   50  \n",
       "9           33286.482830  0.88389   50  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar_size = 50 \n",
    "df[\"tick_group\"] = (\n",
    "    pd.Series(list(range(len(df))))\n",
    "    .div(bar_size)\n",
    "    .apply(np.floor)\n",
    "    .astype(int)\n",
    "    .values\n",
    ")\n",
    "df_grouped_ticks = df.groupby(\"tick_group\")\n",
    "tick_bars = get_bars(df_grouped_ticks, add_time=True)\n",
    "tick_bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Get volume bars:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>vwap</th>\n",
       "      <th>vol</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-02-23 19:42:19.499</td>\n",
       "      <td>33285.50</td>\n",
       "      <td>33286.84</td>\n",
       "      <td>33246.90</td>\n",
       "      <td>33250.00</td>\n",
       "      <td>33264.436711</td>\n",
       "      <td>0.99446</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-02-23 19:42:31.215</td>\n",
       "      <td>33246.91</td>\n",
       "      <td>33253.07</td>\n",
       "      <td>33226.18</td>\n",
       "      <td>33226.87</td>\n",
       "      <td>33240.193454</td>\n",
       "      <td>0.86169</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-02-23 19:43:04.416</td>\n",
       "      <td>33232.23</td>\n",
       "      <td>33244.71</td>\n",
       "      <td>33230.04</td>\n",
       "      <td>33239.77</td>\n",
       "      <td>33232.363594</td>\n",
       "      <td>1.12313</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-02-23 19:44:01.139</td>\n",
       "      <td>33240.00</td>\n",
       "      <td>33250.30</td>\n",
       "      <td>33215.73</td>\n",
       "      <td>33240.10</td>\n",
       "      <td>33232.135781</td>\n",
       "      <td>0.71425</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-02-23 19:45:12.130</td>\n",
       "      <td>33240.10</td>\n",
       "      <td>33258.29</td>\n",
       "      <td>33216.23</td>\n",
       "      <td>33241.21</td>\n",
       "      <td>33238.411739</td>\n",
       "      <td>1.25593</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022-02-23 19:45:15.146</td>\n",
       "      <td>33241.21</td>\n",
       "      <td>33241.21</td>\n",
       "      <td>33232.03</td>\n",
       "      <td>33233.13</td>\n",
       "      <td>33234.773416</td>\n",
       "      <td>0.83403</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022-02-23 19:45:21.810</td>\n",
       "      <td>33233.13</td>\n",
       "      <td>33253.41</td>\n",
       "      <td>33233.13</td>\n",
       "      <td>33248.69</td>\n",
       "      <td>33236.027748</td>\n",
       "      <td>1.21260</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022-02-23 19:46:23.762</td>\n",
       "      <td>33248.69</td>\n",
       "      <td>33286.04</td>\n",
       "      <td>33242.90</td>\n",
       "      <td>33281.10</td>\n",
       "      <td>33270.171474</td>\n",
       "      <td>0.99072</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2022-02-23 19:48:32.173</td>\n",
       "      <td>33280.33</td>\n",
       "      <td>33333.98</td>\n",
       "      <td>33273.90</td>\n",
       "      <td>33301.66</td>\n",
       "      <td>33300.233202</td>\n",
       "      <td>0.98864</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022-02-23 19:50:36.613</td>\n",
       "      <td>33301.65</td>\n",
       "      <td>33322.00</td>\n",
       "      <td>33274.39</td>\n",
       "      <td>33283.79</td>\n",
       "      <td>33296.304224</td>\n",
       "      <td>0.94414</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2022-02-23 19:50:59.291</td>\n",
       "      <td>33283.81</td>\n",
       "      <td>33294.20</td>\n",
       "      <td>33268.00</td>\n",
       "      <td>33268.52</td>\n",
       "      <td>33280.873796</td>\n",
       "      <td>0.28348</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             time      open      high       low     close  \\\n",
       "vol_group                                                                   \n",
       "0         2022-02-23 19:42:19.499  33285.50  33286.84  33246.90  33250.00   \n",
       "1         2022-02-23 19:42:31.215  33246.91  33253.07  33226.18  33226.87   \n",
       "2         2022-02-23 19:43:04.416  33232.23  33244.71  33230.04  33239.77   \n",
       "3         2022-02-23 19:44:01.139  33240.00  33250.30  33215.73  33240.10   \n",
       "4         2022-02-23 19:45:12.130  33240.10  33258.29  33216.23  33241.21   \n",
       "5         2022-02-23 19:45:15.146  33241.21  33241.21  33232.03  33233.13   \n",
       "6         2022-02-23 19:45:21.810  33233.13  33253.41  33233.13  33248.69   \n",
       "7         2022-02-23 19:46:23.762  33248.69  33286.04  33242.90  33281.10   \n",
       "8         2022-02-23 19:48:32.173  33280.33  33333.98  33273.90  33301.66   \n",
       "9         2022-02-23 19:50:36.613  33301.65  33322.00  33274.39  33283.79   \n",
       "10        2022-02-23 19:50:59.291  33283.81  33294.20  33268.00  33268.52   \n",
       "\n",
       "                   vwap      vol  cnt  \n",
       "vol_group                              \n",
       "0          33264.436711  0.99446   85  \n",
       "1          33240.193454  0.86169   46  \n",
       "2          33232.363594  1.12313   33  \n",
       "3          33232.135781  0.71425   72  \n",
       "4          33238.411739  1.25593   44  \n",
       "5          33234.773416  0.83403   15  \n",
       "6          33236.027748  1.21260   20  \n",
       "7          33270.171474  0.99072   45  \n",
       "8          33300.233202  0.98864   69  \n",
       "9          33296.304224  0.94414   53  \n",
       "10         33280.873796  0.28348   18  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar_size = 1 \n",
    "df[\"cum_qty\"] = df[\"qty\"].cumsum()\n",
    "df[\"vol_group\"] = (\n",
    "    df[\"cum_qty\"]\n",
    "    .div(bar_size)\n",
    "    .apply(np.floor)\n",
    "    .astype(int)\n",
    "    .values\n",
    ")\n",
    "df_grouped_ticks = df.groupby(\"vol_group\")\n",
    "volume_bars = get_bars(df_grouped_ticks, add_time=True)\n",
    "volume_bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. Get dollar bars:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>vwap</th>\n",
       "      <th>vol</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-02-23 19:42:29.230</td>\n",
       "      <td>33285.50</td>\n",
       "      <td>33286.84</td>\n",
       "      <td>33239.96</td>\n",
       "      <td>33239.96</td>\n",
       "      <td>33258.572184</td>\n",
       "      <td>1.42997</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-02-23 19:43:17.962</td>\n",
       "      <td>33239.96</td>\n",
       "      <td>33244.71</td>\n",
       "      <td>33226.18</td>\n",
       "      <td>33231.41</td>\n",
       "      <td>33233.221296</td>\n",
       "      <td>1.57424</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-02-23 19:44:58.211</td>\n",
       "      <td>33231.40</td>\n",
       "      <td>33250.30</td>\n",
       "      <td>33215.73</td>\n",
       "      <td>33231.33</td>\n",
       "      <td>33233.232079</td>\n",
       "      <td>1.49104</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-02-23 19:45:15.146</td>\n",
       "      <td>33227.68</td>\n",
       "      <td>33258.29</td>\n",
       "      <td>33226.31</td>\n",
       "      <td>33233.13</td>\n",
       "      <td>33238.544792</td>\n",
       "      <td>1.28824</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-02-23 19:45:59.597</td>\n",
       "      <td>33233.13</td>\n",
       "      <td>33286.04</td>\n",
       "      <td>33233.13</td>\n",
       "      <td>33280.92</td>\n",
       "      <td>33243.191526</td>\n",
       "      <td>1.70740</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022-02-23 19:48:32.173</td>\n",
       "      <td>33279.94</td>\n",
       "      <td>33333.98</td>\n",
       "      <td>33266.54</td>\n",
       "      <td>33301.66</td>\n",
       "      <td>33293.331944</td>\n",
       "      <td>1.48456</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022-02-23 19:50:59.291</td>\n",
       "      <td>33301.65</td>\n",
       "      <td>33322.00</td>\n",
       "      <td>33268.00</td>\n",
       "      <td>33268.52</td>\n",
       "      <td>33292.741055</td>\n",
       "      <td>1.22762</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               time      open      high       low     close  \\\n",
       "value_group                                                                   \n",
       "0           2022-02-23 19:42:29.230  33285.50  33286.84  33239.96  33239.96   \n",
       "1           2022-02-23 19:43:17.962  33239.96  33244.71  33226.18  33231.41   \n",
       "2           2022-02-23 19:44:58.211  33231.40  33250.30  33215.73  33231.33   \n",
       "3           2022-02-23 19:45:15.146  33227.68  33258.29  33226.31  33233.13   \n",
       "4           2022-02-23 19:45:59.597  33233.13  33286.04  33233.13  33280.92   \n",
       "5           2022-02-23 19:48:32.173  33279.94  33333.98  33266.54  33301.66   \n",
       "6           2022-02-23 19:50:59.291  33301.65  33322.00  33268.00  33268.52   \n",
       "\n",
       "                     vwap      vol  cnt  \n",
       "value_group                              \n",
       "0            33258.572184  1.42997  103  \n",
       "1            33233.221296  1.57424   66  \n",
       "2            33233.232079  1.49104   87  \n",
       "3            33238.544792  1.28824   39  \n",
       "4            33243.191526  1.70740   43  \n",
       "5            33293.331944  1.48456   91  \n",
       "6            33292.741055  1.22762   71  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar_size = 50000 \n",
    "df[\"cum_value\"] = df[\"quoteQty\"].cumsum()\n",
    "df[\"value_group\"] = (\n",
    "    df[\"cum_value\"]\n",
    "    .div(bar_size)\n",
    "    .apply(np.floor)\n",
    "    .astype(int)\n",
    "    .values\n",
    ")\n",
    "df_grouped_ticks = df.groupby(\"value_group\")\n",
    "dollar_bars = get_bars(df_grouped_ticks, add_time=True)\n",
    "dollar_bars"
   ]
  }
 ],
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